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Modeling

Modeling Water-Stressed Cotton Growth Using Within-Season Remote Sensing Data

^a Texas A&M Univ., Texas Agric. Exp. Stn. at Uvalde, 1619 Garner Field Rd., Uvalde, TX 78801-6205
^b Texas Tech Univ. Plant and Soil Science, 3810 4th St., Lubbock, TX 79415
^c USDA-ARS Cropping Systems Research Lab., 3810 4th St., Lubbock, TX 79415

^* Corresponding author (jko{at}ag.tamu.edu)

Received for publication October 10, 2005. Remotely sensed crop reflectance data can be used to simulate crop growth using within-season calibration. A model based on GRAMI, previously modified to simulate cotton (Gossypium hirsutum L.) growth, was revised and tested to simulate leaf area development and to estimate lint yield of water-stressed cotton. To verify the model, cotton field data, such as leaf area index (LAI), lint yield, and remotely sensed vegetation indices (VI), were obtained from an experimental field treated with various irrigation levels at the Plant Stress and Water Conservation Laboratory at Lubbock, Texas from 2002 to 2004. The model was validated using field data obtained separately from verification data at the same location in 2005. A hand-held multispectral radiometer with 16 spectral bands was used to measure reflectance. Five VI designs of interest were evaluated and used as input values for within-season calibration of the model. Simulated VI and LAI were in agreement with the measured VI and LAI, with r² values from 0.96 to 0.97 and RMSE values from 0.02 to 0.24 in validation. Simulated lint yields were in agreement with measured lint yields, with r² values from 0.63 to 0.67 and RMSE values from 28.3 to 100.0. The model was not very sensitive to the higher irrigation treatments in reproducing lint yield. We believe that validation with more data sets can deal with this matter. The VI worked equally well in reproducing measured cotton growth when they were used for within-season calibration. The results of this calibration scheme suggest that remote sensing data could be used to adjust modeled cotton growth for various water-stressed conditions.

Abbreviations: BW, bandwidth • CWB, center waveband • DOY, day of year • GDD, growing degree days • LAI, leaf area index • MTVI, modified triangular vegetation index • NIR, near-infrared • OSAVI, optimized soil-adjusted vegetation index • PAR, photosynthetically active radiation • PVI, perpendicular vegetation index • VI, vegetation index

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